updating dim red

2019-10-21 23:23:39 +02:00
parent 46aa974be9
commit d6fbee1781
8 changed files with 12 additions and 6 deletions
@@ -340,7 +340,8 @@ We have a data set defined by a design/feature matrix $\bm{X}$ (see below for it
 !split
 ===== Introducing the Covariance and Correlation functions  =====

-Before we discuss the PCA theorem, we need to remind ourselves about the definition of the covariance and the correlation function.
+Before we discuss the PCA theorem, we need to remind ourselves about
+the definition of the covariance and the correlation function.

 Suppose we have defined two vectors
 $\hat{x}$ and $\hat{y}$ with $n$ elements each. The covariance matrix $\bm{C}$ is defined as